In recent years, CNN has achieved great success in the computer vision task including crowd counting. Boominathan et al. [1]} combined the deep and shallow CNN to extract large scale features to overcome the scale variation. Zhang et al. [2]} proposed a three-branch convolutional neural network to encode the density map with different scales. Based on Zhang's work, Sam et al. [3]} designed a classifier to select the most appropriate branch to generate the density map according to the density variation in each image. Similarly, Hossain et al. [4]} proposed a scale-aware method to make each branch in their model focus on a particular scale. Mahmood et al. [5]} designed an Encoder-Decoder CNN to learn the head heatmap where human heads can be segmented. Shen et al. [6]} introduced the scale-consistency model to alleviate the blurry effects in the estimated density map. Sam et al. [7]} designed the TDF-CNN which contains the bottom-up and top-down CNNs to obtain initial accurate predictions. Marsden et al. [8]} proposed a CNN model that can count the crowd, wildlife, vehicles, and cells. Wang et al. [9]} designed a collector and labeler to generate the crowd data with corresponding labels to reduce the overfitting caused by limited training crowd data. Xu et al. [10]} proposed a segmentation branch based on the probability to guide their framework to encode the final density map. In this paper, we propose a novel segmentation attention mechanism to guide the end-to-end architecture called SegCrowdNet to pay more attention to the human head region. Our segmentation is based on the dice coefficient and we employ the CNN and ground-truth density map to finetune the final density map. Moreover, our proposed SegCrowdNet can adapt the variation of crowd counts by a classification task.
